This article explains the application of knowledge management for project risk management in industry. Combination of knowledge management and risk management is becoming a dire need for industries nowadays, because it has become necessary to make information reach timely to its destined users to achieve the desired goals. Quick decisions are needed throughout a project life cycle to mitigate or avoid a risk, but they are only possible when knowledge about it is in hand and can be inferred for fruitful decisions. Quality engineers make huge effort in analyzing and mitigating the risk and prepare various documents about different risk management stages. But this knowledge resides in documents or underutilized databases without any relation to each other that makes it useless for complex decision making. This article shall explain how knowledge management activities are helpful in risk management and the advantages of their fusion. It will also present a conceptual architecture of an Information Technology based solution for risk management and knowledge management combination.
Enterprises are realizing that their core asset in 21st century is knowledge. In an organization knowledge resides in databases, knowledge bases, filing cabinets and peoples' head. Organizational knowledge is distributed in nature and its poor management causes repetition of activities across the enterprise. To get true benefits from this asset, it is important for an organization to “know what they know”. That’s why many organizations are investing a lot in managing their knowledge. Artificial intelligence techniques have a huge contribution in organizational knowledge management. In this article we are reviewing the applications of ontologies in knowledge management realm
It often happens in teaching that due to complexity of a subject or unavailability of an expert instructor the subject undergoes in a situation that not only affects its outcome but the involvement and learning development of students also. Although contents are covered even in such a situation but their inadequate explanation leaves many question marks in students’ mind. Artificial Intelligence helps represent knowledge graphically and symbolically which can be logically inferred. Visual and symbolic representation of knowledge is easy to understand for both teachers and students. To facilitate students understanding teachers often structure domain knowledge in a visual form where all important contents of a subject can be seen along with their relation to each other. These structures are called ontology which is an important aspect of knowledge engineering. Teaching via ontology is in practice since last two decades. Natural Language Processing (NLP) is a combination of computation and linguistic and is often hard to teach. Its contents are apparently not tied together in a reasonable way which makes it difficult for a teacher that where to start with. In this article we will discuss the design of ontology to support rational learning and efficient teaching of NLP at introductory level.